# Topometric Localization with Deep Learning

arXiv: https://arxiv.org/abs/1706.08775 · HTML: https://mlsanity.com/paper/1706.08775/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness](https://mlsanity.com/paper/2107.07707/) (2021)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [Unsupervised Learning of Depth, Camera Pose and Optical Flow from Monocular Video](https://mlsanity.com/paper/2205.09821/) (2022)

## Abstract (arXiv)

Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an accuracy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. We evaluate the approach on a new challenging pedestrian-based dataset captured over the course of six months in varying weather conditions with a high degree of noise. The experiments demonstrate that the localization errors are up to 10 times smaller than with traditional vision-based localization methods.